New method uses neural networks to enhance Hamiltonian Monte Carlo for inference

Researchers have introduced Neural Surrogate HMC, a novel method that integrates neural likelihood estimation with Hamiltonian Monte Carlo for simulation-based inference. This approach leverages neural networks to approximate likelihood functions, offering advantages in amortizing computations, providing gradients for Hamiltonian Monte Carlo, and smoothing noisy simulation results. The method was successfully applied to model the heliospheric transport of galactic cosmic rays, enabling efficient inference of latent parameters within the Parker equation. AI

IMPACT This method could improve the efficiency and accuracy of complex simulations and parameter inference in scientific research.

RANK_REASON The cluster contains a research paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• artificial neural network
• arXiv
• Hamiltonian Monte Carlo
• Linnea Wolniewicz M
• Markov chain Monte Carlo
• Neural Likelihood Estimation
• Parker equation
• Simulation-Based Inference

• paper
• infra

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